Empirical Investigation of an Equity Pairs Trading Strategy

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The pairs trade or pair trading is a market neutral trading strategy enabling traders to profit from virtually any market conditions: This strategy is categorized as a statistical arbitrage and convergence trading strategy. The strategy monitors performance of two historically correlated securities. When the correlation between the two securities temporarily weakens, i. Pairs trading strategy demands good position sizing, market timingand decision making skill. Although the strategy does not have much downside riskthere 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. 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, he 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 idea as in the past during the holding period of the stock. 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.

Among those suitable for pairs trading are Ornstein-Uhlenbeck models, [5] [9] autoregressive moving average ARMA models [10] and vector error correction models. The success of pairs trading depends heavily on the modeling and forecasting of the spread time series.

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. 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. Trading pairs is not a risk-free strategy. The difficulty comes when prices of the two securities begin to drift apart, i.

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. From Wikipedia, the free encyclopedia.

This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-expertswithout removing the technical details. November Learn how and when to remove this template message. Karlsruhe Institute of Technology. Retrieved 20 January An Introduction to the Cointelation Model". A Guide to Financial Data Analysis". University of Sydney, A Stochastic Control Approach".

Proceedings of the American Control Conference, Monash University, Working Paper. Primary market Secondary market Third market Fourth market.

Common stock Golden share Preferred stock Restricted stock Tracking stock. Authorised capital Issued shares Shares outstanding Treasury stock. Electronic communication network List of stock exchanges Trading hours Multilateral trading facility Over-the-counter.

Alpha Arbitrage pricing theory Beta Bid—ask spread Book value Capital asset pricing model Capital market line Dividend discount model Dividend yield Earnings per share Earnings yield Net asset value Security characteristic line Security market line T-model.

Algorithmic trading Buy and hold Contrarian investing Day trading Dollar cost averaging Efficient-market hypothesis Fundamental analysis Growth stock Market timing Modern portfolio theory Momentum investing Mosaic theory Pairs trade Post-modern portfolio theory Random walk hypothesis Sector rotation Style investing Swing trading Technical analysis Trend following Value investing.

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The pairs trading concept is straight forward - find 2 stocks which have prices that moved historically together and bet on convergence when the spread between them widens. This relative arbitrage should exhibit positive return expectation as there is usually high probability of convergence.

The basic strategy as mentioned in Gatev, Goetzmann, Rouwenhorst, has become popular therefore these simple rules are still profitable but profits are slowly eroding. Lots of new improvements in strategy therefore emerged in last few years we could mention for example Do, Faff, As prices in pair of stocks were closely cointegrated in past, there is high probability that those two securities share common sources of fundamental return correlations.

A temporary shock could move one stock out of the common price band which presents statistical arbitrage opportunity. The universe of pairs is continuously updated which ensures that pairs which no longer move in synchronicity are removed from trading, and only pairs with high probability of convergence remains. Illiquid stocks are removed from the investment universe. Pairs are formed over a twelve-month period formation period and are then traded in next six-month period trading period.

The matching partner for each stock is found by looking for the security that minimizes the sum of squared deviations between two normalized price series. Top 20 pairs with the smallest historical distance measure are then traded and long-short position is opened when pair prices have diverged by two standard deviations and the position is closed when prices revert back.

Performance of a Relative Value Arbitrage Rule http: We test a Wall Street investment strategy, pairs trading, with daily data over Stocks are matched into pairs with minimum distance between normalized historical prices. A simple trading rule yields average annualized excess returns of up to 11 percent for selffinancing portfolios of pairs.

The profits typically exceed conservative transaction costs estimates. Bootstrap results suggest that the pairs effect differs from previously-documented reversal profits. Robustness of the excess returns indicates that pairs trading profits from temporary mis-pricing of close substitutes.

We link the profitability to the presence of a common factor in the returns, different from conventional risk measures. Does simple pairs trading still work? Extending their original analysis to June , we confirm a continuation of the declining trend in profitability. However, contrary to popular belief, we find that the rise in hedge fund activity is not a plausible explanation for the decline. Instead, we observe that the underlying convergence properties are less reliable - there is an increased probability that a pair of close substitutes over the past 12 months are no longer close substitutes in the subsequent half year.

This fragility in the Law of One Price dynamics reflects increased fundamental risks, or uncertainty in market perception of relative values of the paired securities. Nevertheless, we still find more than half the selected pairs are either profitable or very profitable. Losing Sight of the Trees for the Forest? Pairs Trading and Attention Shifts http: This paper tests asset pricing implications of the investor attention shift hypothesis proposed in recent theoretical work.

Our objective is to directly assess how the dynamics of investor inattention affect the relative pricing efficiency of linked assets. We create a novel proxy for investor distraction in the time series and explore its impact in a promising and so far widely neglected setup: Stock pairs trading Gatev , a popular proprietary relative value arbitrage approach.

Relying on almost 50 years of daily data for the US stock market as well as on evidence from eight major international stock markets, we provide broad and robust evidence for substantial distraction effects. For instance, the average one-month return on long-short US stock pairs that open on high distraction days is about twice as high as the return on pairs that open on low distraction days.

A number of conceptually quite diverse tests further lend support to the idea of time-varying investor attention being an important source of friction in financial markets. We show that an equity pairs trading strategy generates large and significant abnormal returns. We then examine the economic drivers of this strategy. First, we find that this return is not driven purely by the short-term reversal of returns.

Second, we decompose the pair-wise stock return correlations into those that can be explained by common factors such as size, book-to-market, and accruals and those that cannot. We find that the pairs correlations explainable by common factors drive most of the pairs trading returns. Third, the value-weighted profits of pairs trading are higher in firms in a richer information environment, and our trading strategy performs poorly in the recent liquidity crisis, suggesting that the pairs trading profits are not primarily driven by the delay in information diffusion and liquidity provision.

Finally, consistent with the adaptive market efficiency theory, the return to this simple pairs trading strategy has diminished over time. European Equity Pairs Trading: This article examines an equity pairs trading strategy using daily, weekly and monthly European share price data over the period Bootstrap results suggest returns from the strategy are attributable to skill rather than luck, while insignificant beta coefficients provide evidence that this is a market neutral strategy.

The relative value arbitrage rule "pairs trading" is a well-established speculative investment strategy on financial markets, dating back to the s. Based on relative mispricing between a pair of stocks, pairs trading strategies create excess returns if the spread between two normally comoving stocks is away from its equilibrium path and is assumed to be mean reverting.

To overcome the problem of detecting temporary in contrast to longer lasting deviations from spread equilibrium, this paper bridges the literature on Markov regime-switching and the scientific work on statistical arbitrage. Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy http: Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean- reverting spreads with a certain degree of predictability.

This paper applies cointegration tests to identify stocks to be used in pairs trading strategies. In addition to estimating long-term equilibrium and to model the resulting residuals, we select stock pairs to compose a pairs trading portfolio based on an indicator of profitability evaluated in-sample.

Empirical analysis shows that the proposed strategy exhibit excess returns of Risk and Return http: In this paper we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy.

Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance.

To evaluate the effect of market frictions on the strategy we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states.

Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk adjusted returns are generally not significantly different from zero.

On the Persistence of Cointegration in Pairs Trading http: An exploratory study is conducted to assess the persistence of cointegration among U. In other words, if a pair of equities is found to be cointegrated in one period, is it likely that it will be found to be cointegrated in the subsequent period? The evidence does not support the hypothesis that cointegration is a persistent property.

On the Determinants of Pairs Trading Profitability http: We perform a large-scale empirical analysis of pairs trading, a popular relative-value arbitrage approach. We start with a cross-country study of 34 international stock markets and uncover that abnormal returns are a persistent phenomenon. We then construct a comprehensive U.

Our findings indicate that the type of news leading to pair divergence, the dynamics of investor attention as well as the dynamics of limits to arbitrage are important drivers of the strategy's time-varying performance. Motivated by the industry practice of pairs trading, we study the optimal timing strategies for trading a mean-reverting price spread. An optimal double stopping problem is formulated to analyze the timing to start and subsequently liquidate the position subject to transaction costs.

Modeling the price spread by an Ornstein-Uhlenbeck process, we apply a probabilistic methodology and rigorously derive the optimal price intervals for market entry and exit. As an extension, we incorporate a stop-loss constraint to limit the maximum loss. We show that the entry region is characterized by a bounded price interval that lies strictly above the stop-loss level. As for the exit timing, a higher stop-loss level always implies a lower optimal take-profit level.

Both analytical and numerical results are provided to illustrate the dependence of timing strategies on model parameters such as transaction cost and stop-loss level.

Xie, Liew, Wu, Zou: Pairs Trading with Copulas http: Pairs trading is a well-acknowledged speculative investment strategy that is widely used in the financial markets, and distance method is the most commonly implemented pairs trading strategy by traders and hedge funds. However, this approach, which can be seen as a standard linear correlation analysis, is only able to fully describe the dependency structure between stocks under the assumption of multivariate normal returns.

To overcome this limitation, we propose a new pairs trading strategy using copula modeling technique. Copula allows separate estimation of the marginal distributions of stock returns as well as their joint dependency structure. Thus, the proposed new strategy, which is based on the estimated optimal dependency structure and marginal distributions, can identify relative undervalued or overvalued positions with more accuracy and confidence. Hence, it is deemed to generate more trading opportunities and profits.

A simple one-pair-one-cycle example is used to illustrate the advantages of the proposed method. Besides, a large sample analysis using the utility industry data is provided as well. The overall empirical results have verified that the proposed strategy can generate higher profits compared with the conventional distance method.

We argue that the proposed trading strategy can be considered as a generalization of the conventional pairs trading strategy. Improving Pairs Trading http: It investigates if the profitability of pairs opening after an above average volume day in one of the assets are distinct in returns characteristics and if the introduction of a limit on the days the pair is open can improve the strategy returns. Results suggest that indeed pairs opening after a single sided shock are less profitable and that a limitation on the numbers of days a pair is open can significantly improve the profitability by as much as 30 basis points per month.

Rad, Yew Low, Faff: The Profitability of Pairs Trading Strategies: Distance, Cointegration, and Copula Methods http: We examine and compare the performance of three different pairs trading strategies - the distance cointegration, and copula methods - on the US equity market from to using a time-varying series of trading costs.

Using various performance measures, we conclude that cointegration strategy performs as well as the distance method.

However, the copula method shows relatively poor performance.