Pairs Trading with Stocks - QuantPedia (2024)

Pairs Trading with Stocks

The practice often shows that profitable trading strategies do not have to be complicated; a good example is a well known Pairs Trading with Stocks. The Pairs Trading is a popular short-term speculation strategy with a long history on Wall Street. However, as was previously mentioned, the concept of pairs trading is straightforward. A potential investor has to find two stocks whose prices have moved together historically, and when the spread between them widens, short the winner and buy the loser. The profits lie in the assumption that history would repeat. If history repeats itself, prices will converge, and the arbitrageur will profit. To sum it up, this strategy is based solely on simple contrarian principles and past stock prices: Said, the strategy bets on convergence when the spread between stocks widens.
Additionally, the same pattern was found in the European markets. Lucey and Walshe in the “European Equity Pairs Trading: The Effect of Data Frequency on Risk and Return” examined an equity pairs trading strategy using daily, weekly and monthly European share price data over the period 1998-2007. The authors show that when stocks are matched into pairs with minimum distance between normalized historical prices, a simple trading rule based on volatility between these prices yields annualized raw returns of up to 15% for the weekly data frequency.
On a less positive note, more recent research states that the positive returns of this strategy are slowly diminishing. For example, Chen, Chen, and Li in the “Empirical Investigation of an Equity Pairs Trading Strategy“, have also shown while using past data that an equity pairs trading strategy generates large and significant abnormal returns. However, in the end, they said that consistent with the adaptive market efficiency theory, the return to this simple pairs trading strategy has diminished over time. Eroding profits have led academics to improve their strategy. As an example, we would like to mention the paper “Does simple pairs trading still work?” written by Do and Faff (the paper can be found in the “Other Papers” section).

Fundamental reason

Pioneer of this strategy, Nunzio Tartaglia states that the explanation of the pairs trading is psychological. He claims, that “Human beings don’t like to trade against human nature, which wants to buy stocks after they go up not down.” This means that pairs traders are the disciplined investors taking advantage of the undisciplined over-reaction displayed by individual investors.
The profits could also be explained by some logical assumptions that result in the high expected probability of future returns of the Pairs Trading portfolio. If prices of some stock pair in the past were closely cointegrated, there is a high probability that those two securities share common sources of fundamental return correlations. However, a temporary shock could move one stock out of the common price band, which presents a statistical arbitrage opportunity. Additionally, the universe of pairs is continuously updated, and this ensures that pairs which no longer move in synchronicity are removed from trading. Therefore, the portfolio includes only pairs with a high probability that their prices would be convergent. Moreover, the authors ruled out several explanations for the pairs trading profits, including mean-reversion as previously documented in the literature, unrealized bankruptcy risk, and the inability of arbitrageurs to take advantage of the profits due to short-sale constraints.
Chen, Chen, and Li in the “Empirical Investigation of an Equity Pairs Trading Strategy” have examined the economic drivers of this strategy. First, they have found that this return is not driven purely by the short-term reversal of returns. Secondly, they have decomposed 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. Quoting the authors: “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.” The last only underlines the need for the enhanced Pair Trading strategy – for example, the work of Do and Faff.

Keywordsarbitrageequity long shortpairs trading

Simple trading strategy

The investment universe consists of stocks from NYSE, AMEX, and NASDAQ, while illiquid stocks are removed from the investment universe. Cumulative total return index is then created for each stock (dividends included), and the starting price during the formation period is set to $1 (price normalization). Pairs are formed over twelve months (formation period) and are then traded in the 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 a long-short position is opened when pair prices have diverged by two standard deviations, and the position is closed when prices revert.

Hedge for stocks during bear markets

Yes - Pairs trading strategy is related to other reversal strategies in the equity market (see #13 – Short Term Reversal in Stocks) – it is also a type of “liquidity providing” strategy. As such, it usually performs well during market crises. We recommend a research paper written by Bowen, Hutchinson: “Pairs Trading in the UK Equity Market: Risk and Return” for more insights into strategy. Bowen and Hutchinson test pairs trading strategy during the two most extreme crises (October 1987 and Autumn 2008) and show that the strategy delivered significantly positive returns. Again these results imply that the strategy benefits from increased volatility or a drop in liquidity.

Source paper

Out-of-sample strategy's implementation/validation in QuantConnect's framework (chart+statistics+code)

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