Pairs trade

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Example of pair trade graphical representation Pair tool.JPG
Example of pair trade graphical representation

A pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: uptrend, downtrend, or sideways movement. This strategy is categorized as a statistical arbitrage and convergence trading strategy. [1] Pair trading was pioneered by Gerry Bamberger and later led by Nunzio Tartaglia's quantitative group at Morgan Stanley in the 1980s. [2]

Contents

The strategy monitors performance of two historically correlated securities. When the correlation between the two securities temporarily weakens, i.e. one stock moves up while the other moves down, the pairs trade would be to short the outperforming stock and to long the underperforming one, betting that the "spread" between the two would eventually converge. [3] The divergence within a pair can be caused by temporary supply/demand changes, large buy/sell orders for one security, reaction for important news about one of the companies, and so on.

Pairs trading strategy demands good position sizing, market timing, and decision making skill. Although the strategy does not have much downside risk, there is a scarcity of opportunities, and, for profiting, the trader must be one of the first to capitalize on the opportunity.

A notable pairs trader was hedge fund Long-Term Capital Management; [4] see Dual-listed companies.

Model-based pairs trading

Example of a portfolio spread forecast using an ARMA model and the associated forecast error bounds Forecast pra rli.jpg
Example of a portfolio spread forecast using an ARMA model and the associated forecast error bounds

While it is commonly agreed that individual stock prices are difficult to forecast, there is evidence suggesting that it may be possible to forecast the price—the spread series—of certain stock portfolios. A common way to attempt this is by constructing the portfolio such that the spread series is a stationary process. To achieve spread stationarity in the context of pairs trading, where the portfolios only consist of two stocks, one can attempt to find a cointegration irregularities between the two stock price series who generally show stationary correlation. This irregularity is assumed to be bridged soon and forecasts are made in the opposite nature of the irregularity. [5] [6] This would then allow for combining them into a portfolio with a stationary spread series. [7] Regardless of how the portfolio is constructed, if the spread series is a stationary processes, then it can be modeled, and subsequently forecast, using techniques of time series analysis. Among those suitable for pairs trading are Ornstein-Uhlenbeck models, [8] [9] autoregressive moving average (ARMA) models [10] and (vector) error correction models. [7] Forecastability of the portfolio spread series is useful for traders because:

  1. The spread can be directly traded by buying and selling the stocks in the portfolio, and
  2. The forecast and its error bounds (given by the model) yield an estimate of the return and risk associated with the trade.

The success of pairs trading depends heavily on the modeling and forecasting of the spread time series. [11] Comprehensive empirical studies on pairs trading have investigated its profitability over the long-term in the US market using the distance method, co-integration, and copulas. They have found that the distance and co-integration methods result in significant alphas and similar performance, but their profits have decreased over time. Copula pairs trading strategies result in more stable but smaller profits. [12]

Algorithmic pairs trading

Today, pairs trading is often conducted using algorithmic trading strategies on an execution management system. These strategies are typically built around models that define the spread based on historical data mining and analysis. The algorithm monitors for deviations in price, automatically buying and selling to capitalize on market inefficiencies. The advantage in terms of reaction time allows traders to take advantage of tighter spreads.

Market neutrality

Drift and risk management

Trading pairs is not a risk-free strategy. The difficulty comes when prices of the two securities begin to drift apart, i.e. the spread begins to trend instead of reverting to the original mean. Dealing with such adverse situations requires strict risk management rules, which have the trader exit an unprofitable trade as soon as the original setup—a bet for reversion to the mean—has been invalidated. This can be achieved, for example, by forecasting the spread and exiting at forecast error bounds. A common way to model, and forecast, the spread for risk management purposes is by using autoregressive moving average models.

Some other risks include:

A simplified example

Pepsi (PEP) and Coca-Cola (KO) are different companies that create a similar product, soda pop. Historically, the two companies have shared similar dips and highs, depending on the soda pop market. If the price of Coca-Cola were to go up a significant amount while Pepsi stayed the same, a pairs trader would buy Pepsi stock and sell Coca-Cola stock, assuming that the two companies would later return to their historical balance point. If the price of Pepsi rose to close that gap in price, the trader would make money on the Pepsi stock, while if the price of Coca-Cola fell, they would make money on having shorted the Coca-Cola stock.

The reason for the deviated stock to come back to original value is itself an assumption. It is assumed that the pair will have similar business performance as in the past during the holding period of the stock.

See also

Related Research Articles

In economics and finance, arbitrage is the practice of taking advantage of a difference in prices in two or more markets – striking a combination of matching deals to capitalise on the difference, the profit being the difference between the market prices at which the unit is traded. 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 after transaction costs. For example, an arbitrage opportunity is present when there is the possibility to instantaneously buy something for a low price and sell it for a higher price.

<span class="mw-page-title-main">Day trading</span> Buying and selling financial instruments within the same trading day

Day trading is a form of speculation in securities in which a trader buys and sells a financial instrument within the same trading day, so that all positions are closed before the market closes for the trading day to avoid unmanageable risks and negative price gaps between one day's close and the next day's price at the open. Traders who trade in this capacity are generally classified as speculators. Day trading contrasts with the long-term trades underlying buy-and-hold and value investing strategies. Day trading may require fast trade execution, sometimes as fast as milli-seconds in scalping, therefore direct-access day trading software is often needed.

Market risk is the risk of losses in positions arising from movements in market variables like prices and volatility. There is no unique classification as each classification may refer to different aspects of market risk. Nevertheless, the most commonly used types of market risk are:

A hedge is an investment position intended to offset potential losses or gains that may be incurred by a companion investment. A hedge can be constructed from many types of financial instruments, including stocks, exchange-traded funds, insurance, forward contracts, swaps, options, gambles, many types of over-the-counter and derivative products, and futures contracts.

In finance, statistical arbitrage is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities held for short periods of time. These strategies are supported by substantial mathematical, computational, and trading platforms.

In finance, an investment strategy is a set of rules, behaviors or procedures, designed to guide an investor's selection of an investment portfolio. Individuals have different profit objectives, and their individual skills make different tactics and strategies appropriate. Some choices involve a tradeoff between risk and return. Most investors fall somewhere in between, accepting some risk for the expectation of higher returns. Investors frequently pick investments to hedge themselves against inflation. During periods of high inflation investments such as shares tend to perform less well in real terms.

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.

<span class="mw-page-title-main">Stock trader</span> Person or company involved in trading equity securities

A stock trader or equity trader or share trader, also called a stock investor, is a person or company involved in trading equity securities and attempting to profit from the purchase and sale of those securities. Stock traders may be an investor, agent, hedger, arbitrageur, speculator, or stockbroker. Such equity trading in large publicly traded companies may be through a stock exchange. Stock shares in smaller public companies may be bought and sold in over-the-counter (OTC) markets or in some instances in equity crowdfunding platforms.

Momentum investing is a system of buying stocks or other securities that have had high returns over the past three to twelve months, and selling those that have had poor returns over the same period.

In finance, volatility arbitrage is a term for financial arbitrage techniques directly dependent and based on volatility.

In finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets.

In finance, a calendar spread is a spread trade involving the simultaneous purchase of futures or options expiring on a particular date and the sale of the same instrument expiring on another date. These individual purchases, known as the legs of the spread, vary only in expiration date; they are based on the same underlying market and strike price.

The following outline is provided as an overview of and topical guide to finance:

Trend following or trend trading is a trading strategy according to which one should buy an asset when its price trend goes up, and sell when its trend goes down, expecting price movements to continue.

Option strategies are the simultaneous, and often mixed, buying or selling of one or more options that differ in one or more of the options' variables. Call options, simply known as Calls, give the buyer a right to buy a particular stock at that option's strike price. Opposite to that are Put options, simply known as Puts, which give the buyer the right to sell a particular stock at the option's strike price. This is often done to gain exposure to a specific type of opportunity or risk while eliminating other risks as part of a trading strategy. A very straightforward strategy might simply be the buying or selling of a single option; however, option strategies often refer to a combination of simultaneous buying and or selling of options.

In finance, an option is a contract which conveys to its owner, the holder, the right, but not the obligation, to buy or sell a specific quantity of an underlying asset or instrument at a specified strike price on or before a specified date, depending on the style of the option. Options are typically acquired by purchase, as a form of compensation, or as part of a complex financial transaction. Thus, they are also a form of asset and have a valuation that may depend on a complex relationship between underlying asset price, time until expiration, market volatility, the risk-free rate of interest, and the strike price of the option. Options may be traded between private parties in over-the-counter (OTC) transactions, or they may be exchange-traded in live, public markets in the form of standardized contracts.

<span class="mw-page-title-main">Volatility (finance)</span> Degree of variation of a trading price series over time

In finance, volatility is the degree of variation of a trading price series over time, usually measured by the standard deviation of logarithmic returns.

Mean reversion is a financial term for the assumption that an asset's price will tend to converge to the average price over time.

Portfolio optimization is the process of selecting an optimal portfolio, out of a set of considered portfolios, according to some objective. The objective typically maximizes factors such as expected return, and minimizes costs like financial risk, resulting in a multi-objective optimization problem. Factors being considered may range from tangible to intangible.

Financial correlations measure the relationship between the changes of two or more financial variables over time. For example, the prices of equity stocks and fixed interest bonds often move in opposite directions: when investors sell stocks, they often use the proceeds to buy bonds and vice versa. In this case, stock and bond prices are negatively correlated.

References

  1. Kanamura, Takashi; Rachev, Svetlozar; Fabozzi, FranK (5 July 2008). "The Application of Pairs Trading to Energy Futures Markets" (PDF). Karlsruhe Institute of Technology. Archived from the original (PDF) on 2013-02-05. Retrieved 20 January 2015.
  2. Bookstaber, Richard. A Demon Of Our Own Design, p. 186. Wiley, 2006.
  3. "Lecture 23: Pairs Trading" (PDF).
  4. Lowenstein, Roger (2000). When genius failed : the rise and fall of Long-Term Capital Management (1 ed.). New York: Random House. ISBN   978-0-375-50317-7.
  5. C. Alexander: "Market Models: A Guide to Financial Data Analysis". Wiley, 2001.
  6. "Co-integration Trading Strategy". Bullmen Binary. Retrieved 20 January 2015.
  7. 1 2 A. D. Schmidt: "Pairs Trading - A Cointegration Approach". University of Sydney, 2008. http://ses.library.usyd.edu.au/bitstream/2123/4072/1/Thesis_Schmidt.pdf
  8. Mahdavi Damghani, Babak (2013). "The Non-Misleading Value of Inferred Correlation: An Introduction to the Cointelation Model". Wilmott . 2013 (1): 50–61. doi:10.1002/wilm.10252.
  9. S. Mudchanatongsuk, J. A. Primbs and W. Wong: "Optimal Pairs Trading: A Stochastic Control Approach". Proceedings of the American Control Conference, 2008. http://www.nt.ntnu.no/users/skoge/prost/proceedings/acc08/data/papers/0479.pdf
  10. G. Vidyamurthy: "Pairs trading: quantitative methods and analysis". Wiley, 2004.
  11. "A New Approach to Modeling and Estimation for Pairs Trading". Monash University, Working Paper. http://www.finanzaonline.com/forum/attachments/econometria-e-modelli-di-trading-operativo/1048428d1238757908-spread-e-pair-trading-pairstrading_binhdo.pdf
  12. Rad, Hossein; Low, Rand Kwong Yew; Faff, Robert (2016-04-27). "The profitability of pairs trading strategies: distance, cointegration and copula methods". Quantitative Finance. 16 (10): 1541–1558. doi:10.1080/14697688.2016.1164337. ISSN   1469-7688. S2CID   219717488.